Bibliographic record
Abstract
The author is a graduate of University College, London, Cranfield University and Oxford Brookes University in the UK where he completed his Ph. D thesis about Energy Efficient Housing. He is a registered architect in the UK and has worked for many years as an environmental consultant to the construction industry. In 2003, Dr Gorgolewski moved to Canada to take up a position as Associate Professor at the School of Architectural Science at Ryerson University where he lectures on issues of sustainability, environmental design and building science. Dr Gorgolewski has published several books and many articles and papers on issues of sustainable construction. His past positions include chair of the committee of the Association for Environment Conscious Building, in the UK, secretary to the Steel Sector Committee for Sustainable Construction and member of the Building Research Establishment Certification Technical Board. He has received many grants from public and private institutions to investigate issues of sustainable construction. Currently he is pursuing research in the areas of office building design in the 21st century and the benefits of thermal mass. The text that follows is an edited and revised version of a paper presented at the international symposion on "The Natural City, " Toronto, 23-25 June, 2004, sponsored by the University of Toronto's Division of the Environment, Institute for Environmental Studies, and the World Society for Ekistics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".